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Dictionary Learning

Dictionary Learning is an important problem in multiple areas, ranging from computational neuroscience, machine learning, to computer vision and image processing. The general goal is to find a good basis for given data. More formally, in the Dictionary Learning problem, also known as sparse coding, we are given samples of a random vector $y\in\mathbb{R}^n$, of the form $y=Ax$ where $A$ is some unknown matrix in $\mathbb{R}^{n×m}$, called dictionary, and $x$ is sampled from an unknown distribution over sparse vectors. The goal is to approximately recover the dictionary $A$.

Source: Polynomial-time tensor decompositions with sum-of-squares

Papers

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Showing 661–670 of 823 papers

TitleStatusHype
A Fully Automated Latent Fingerprint Matcher with Embedded Self-learning Segmentation Module—0
A Generative Model for Deep Convolutional Learning—0
A Greedy Approach to _0, Based Convolutional Sparse Coding—0
A Hebbian/Anti-Hebbian Network for Online Sparse Dictionary Learning Derived from Symmetric Matrix Factorization—0
A High-Performance Accelerator for Super-Resolution Processing on Embedded GPU—0
Alignment Distances on Systems of Bags—0
A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning—0
A LSTM Approach with Sub-Word Embeddings for Mongolian Phrase Break Prediction—0
Alternating minimization for dictionary learning: Local Convergence Guarantees—0
Alternating minimization for dictionary learning with random initialization—0
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